As popular generative models, Variational Autoencoder (VAE) [1]} and Generative Adversarial Networks(GAN) [2]} have been applied in disentanglement. For example, InfoGAN [3]}, based on the GAN model, maximizes the mutual information between the small subset of the latent variables and the observations which makes the latent variables contain more information about the real data, hence increases the interpretability of the latent representation. Based on InfoGAN, FineGAN [4]}, [5]} creates a hierarchical architecture that assigns the background, object shape, and object appearance to different hierarchy to generate images of fine-grained object categories. And VAE model, derived from autoencoder [6]} is also widely applied to representation learning, VAEs have been demonstrated their unique power to constrain representations disentanglement. For example, \(\beta \) -VAE [7]}, \(\beta \) -TCVAE [8]}, FactorVAE [9]} and so on [10]} are able to get more disentangled representation.
